Transactions on Machine Learning and Data Mining (ISSN: 1865-6781)

Volume 1 - Number 1 - July 2008 - Pages 17-30

Classification Based on Consistent Itemset Rules

Y. Shidara, M. Kudo and A. Nakamura

Graduate School of Information Science and Technology, Hokkaido University, Japan

Abstract

We propose an approach to build a classifier composing consistent
(100% confident) rules. Recently, associative classifiers that utilize
association rules have been widely studied, and it has been shown that the
associative classifiers often outperform traditional classifiers. In this case,
it is important to collect high-quality (association) rules. Many algorithms find
only rules with high support values, because reducing the minimum support to be
satisfied is computationally demanding. However, it may be effective to collect
rules with low support values but high confidence values. Therefore, we propose
an algorithm that produces a wide variety of 100% confident rules including low
support rules. To achieve this goal, we adopt a specific-to-general rule
searching strategy, in contrast to previous approaches. Our experimental results
show that the proposed method achieves higher accuracies in several datasets
taken from UCI machine learning repository.